Rage Bait Content on Facebook: Mapping the Perceived Impacts and Engagement Behavior of Students at Visayas State University

1 Descriptive

1.1 Degree

## need cleaning
socio_demo_dta |> 
  mutate(degree = str_remove_all(degree, "[0-9]|-")) |>
  mutate(degree = str_trim(degree, side = "both")) |> 
  count(degree, sort = T) |> 
  View()

1.2 Social Media

1.2.1 Platform

## subtitle
plt_subtitle <- str_wrap("Female students reported higher usage across most platforms, with Facebook, TikTok, and Instagram as the top three; male students also favored Facebook but showed comparatively lower engagement overall.", 100)

## plotting
plt_soc_med <- 
  soc_med_dta |>
  count(sex, social_media) |>
  group_by(sex) |>
  mutate(
    pct = n / sum(n),
    pct_lab = str_c("n=", n),
    social_media = reorder_within(social_media, n, sex)
  ) |>
  ggplot(aes(n, social_media, fill = sex)) +
  geom_col(width = 0.7) +
  geom_text(aes(label = pct_lab), hjust = -0.1) +
  scale_y_reordered() +
  scale_x_continuous(limits = c(0, 80)) +
  facet_wrap(~ sex, scales = "free_y") +
  custom_theme +
  labs(
    title = "Which social media platforms do you use most often?",
    subtitle = plt_subtitle,
    fill = element_blank(),
    y = element_blank()
  )

## saving data
ggsave(
  plot = plt_soc_med,
  filename = "plot/soc_med_gender.jpg",
  unit = "in",
  width = 10,
  height = 7
)

## display plot
knitr::include_graphics("plot/soc_med_gender.jpg")

1.3 Social media content

1.3.1 by Gender

## plot subtitle
plt_subtitle <- str_wrap("Entertainment, music, and lifestyle content were the most engaged categories among female students, while males showed stronger engagement with entertainment, music, and gaming; overall, females reported higher engagement across most content types.", 120)

plt_soc_med_content_gender <- 
  soc_med_content_dta |> 
  # filter(!str_detect( social_media, "All of it")) |> 
  count(sex, social_media_content) |> 
  group_by(sex) |> 
  mutate(pct = n / sum(n)) |> 
  mutate(social_media_content = reorder_within(social_media_content, pct, sex)) |> 
  mutate(pct_label = str_c("n=", n, " (", round(pct*100, ), "%)")) |> 
  ggplot(aes(pct, social_media_content, fill = sex)) + 
  geom_col(width = 0.8) +
  geom_text(aes(label = pct_label), hjust = -0.1) +
  scale_x_continuous(limits = c(0, 0.3), labels = scales::percent_format()) +
  scale_y_reordered() +
  facet_wrap(~ sex, scales = "free", ncol = 2) +
  custom_theme +
  labs(
    title = "What kinds of content do you usually engage with on social media?",
    subtitle = plt_subtitle,
    x = element_blank(),
    y = element_blank(),
    fill = element_blank()
  )

  ## saving plot
  ggsave(
    plot = plt_soc_med_content_gender,
    filename = "plot/soc_med_content_gender.jpg",
    unit = "in",
    height = 6,
    width = 12,
    dpi = 400
  )

## display plot
knitr::include_graphics("plot/soc_med_content_gender.jpg")

1.3.2 by social media

## subtitle
plt_subtitle <- str_wrap("Across platforms, entertainment, music, and lifestyle content consistently drew the highest engagement, with Facebook, Instagram, TikTok, and YouTube showing similar patterns; educational and news content also ranked strongly, while politics and gaming remained lower overall.", 120)

plt_soc_med_content <- 
  soc_med_content_dta |> 
  filter(!str_detect( social_media, "All of it")) |> 
  count(social_media, social_media_content) |> 
  group_by(social_media) |> 
  mutate(pct = n / sum(n)) |> 
  mutate(social_media_content = reorder_within(social_media_content, pct, social_media)) |> 
  mutate(pct_label = str_c("n=", n, " (", round(pct*100, ), "%)")) |> 
  ggplot(aes(pct, social_media_content)) + 
  geom_col(width = 0.8) +
  geom_text(aes(label = pct_label), hjust = -0.1) +
  scale_x_continuous(limits = c(0, 0.3), labels = scales::percent_format()) +
  scale_y_reordered() +
  facet_wrap(~ social_media, scales = "free", ncol = 2) +
  custom_theme +
  labs(
    title = "What kinds of content do you usually engage with on social media?",
    subtitle = plt_subtitle,
    x = element_blank(),
    y = element_blank()
  )

  ## saving plot
  ggsave(
    plot = plt_soc_med_content,
    filename = "plot/soc_med_content.jpg",
    unit = "in",
    height = 14,
    width = 12,
    dpi = 400
  )

## display plot
knitr::include_graphics("plot/soc_med_content.jpg")

1.4 Social media hours

soc_med_category |> 
  filter(!str_detect( social_media, "All of it")) |> 
  count(social_media, social_media_hours) |> 
  group_by(social_media) |> 
  mutate(pct = n / sum(n)) |> 
  mutate(social_media_hours = reorder_within(social_media_hours, pct, social_media)) |> 
  mutate(pct_label = str_c("n=", n, " (", round(pct*100, ), "%)")) |> 
  ggplot(aes(pct, social_media_hours)) + 
  geom_col(width = 0.8, position = position_dodge2(preserve = "single")) +
  geom_text(aes(label = pct_label), hjust = -0.1) +
  scale_x_continuous(limits = c(0, 1), labels = scales::percent_format()) +
  scale_y_reordered() +
  facet_wrap(~ social_media, scales = "free", ncol = 2) +
  custom_theme +
  labs(
    title = "What kinds of content do you usually engage with on social media?",
    x = element_blank(),
    y = element_blank()
  )